Builds agentic AI systems, MCP servers, retrieval systems, and evaluation pipelines for reliable AI-generated code. Responsibilities include supporting large-codebase changes, implementing compile and test gates, applying concurrency and telemetry best practices, improving performance and safety, and collaborating on AI workflow experiments. The role requires Java or Python proficiency, production or developer-facing systems experience, strong communication, and a computer science foundation.
About the role-
Tristone is embedding agentic AI
directly into its workflows and systems. As a member of the Agentic AI
engineering team, you'll help build agents, MCP servers, and evaluation loops
that make AI-generated code reliable, auditable, and fast to ship. You'll work
closely with senior members of Tristone to deliver pragmatic, high-impact AI
integrations at scale.
Key Responsibilities-
- Contributing to the platform by implementing agents, MCP servers,
and supporting services that propose and apply changes to large codebases
- Assisting in developing retrieval systems that give AI agents and
developers accurate, up-to-date context from large codebases and design
artifacts
- Helping measure and improve AI-generated changes by building
compile/test/evaluate pipelines (static analysis, style and safety checks,
performance gates, code review)
- Applying Tristone’s best practices in concurrency, telemetry,
configuration hygiene, and performance-sensitive code paths to ensure AI
outputs are reliable and idiomatic
- Collaborating with colleagues on experiments to evaluate and
improve AI-driven workflows
- Complying with IT policies and procedures.
- Maintaining security of information at all times.
Requirements-
- 1+ years of experience
- Proficiency in a high-level
programming language (Java or Python preferred)
- Experience contributing to
developer-facing or production systems
- Curiosity and eagerness to learn
about AI/LLM application patterns, even if you haven't worked closely with
them yet
- Strong communication skills;
comfortable working with engineers in a fast, collaborative environment
Strong
preferred-
- Exposure to AI tools or workflows
(e.g., LangChain, LangGraph, AutoGen, or similar)
- Experience with retrieval systems
(vector search, embeddings, or hybrid approaches)
- Familiarity with compiler/static
analysis or large-scale refactoring tools
- Interest in customizing or
fine-tuning open-weight models
- Knowledge of model-serving or
evaluation infrastructure
Qualification-
- BS+ in Computer Science (or related)
with strong fundamentals (algorithms, data structures, systems)
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